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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """train"""
- from ast import arg
- import os
-
- from mindspore import Model
- from mindspore import context
- from mindspore import nn
- from mindspore.common import set_seed
- from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
-
- from src.args import args
- from src.tools.callback import EvaluateCallBack
- from src.tools.cell import cast_amp
- from src.tools.criterion import get_criterion, NetWithLoss
- from src.tools.get_misc import get_dataset, set_device, get_model, pretrained, get_train_one_step
- from src.tools.optimizer import get_optimizer
-
-
- def sync_data(args, environment="train"):
- if environment == "train":
- workroot = "/home/work/user-job-dir"
- elif environment == "debug":
- workroot = "/home/ma-user/work/"
-
- data_dir = os.path.join(workroot, "data")
- if not os.path.exists(data_dir):
- os.mkdir(data_dir)
-
- train_dir = os.path.join(workroot, "model")
- if not os.path.exists(train_dir):
- os.mkdir(train_dir)
-
- if environment == 'train':
- obs_data_url = args.data_url
- args.data_url = data_dir
-
- try:
- import moxing as mox
- mox.file.copy_parallel(obs_data_url, data_dir)
- print("Successfully Download {} to {}".format(obs_data_url, args.data_url))
- except Exception as e:
- print('moxing download {} to {} failed: '.format(obs_data_url, args.data_url) + str(e))
-
- return train_dir
-
-
- def sync_model(args, environment="train"):
- if environment == "train":
- workroot = "/home/work/user-job-dir"
- elif environment == "debug":
- workroot = "/home/ma-user/work/"
-
- train_dir = os.path.join(workroot, "model")
- if not os.path.exists(train_dir):
- os.mkdir(train_dir)
-
- if environment == 'train':
- obs_train_url = args.train_url
- args.train_url = train_dir
-
- try:
- import moxing as mox
- mox.file.copy_parallel(args.train_url, obs_train_url)
- print("Successfully Upload {} to {}".format(args.train_url, obs_train_url))
- except Exception as e:
- print('moxing upload {} to {} failed: '.format(args.train_url, obs_train_url) + str(e))
-
-
- def main():
- assert args.crop, f"{args.arch} is only for evaluation"
- mode = {
- 0: context.GRAPH_MODE,
- 1: context.PYNATIVE_MODE
- }
- context.set_context(mode=mode[args.graph_mode], device_target=args.device_target)
- context.set_context(enable_graph_kernel=False)
- if args.device_target == "Ascend":
- context.set_context(enable_auto_mixed_precision=True)
- rank = set_device(args)
- set_seed(args.seed + rank)
-
- # get model and cast amp_level
- net = get_model(args)
- cast_amp(net)
- criterion = get_criterion(args)
- net_with_loss = NetWithLoss(net, criterion)
- if args.pretrained:
- pretrained(args, net)
-
- if args.run_modelarts:
- train_dir = "/cache/ckpt_" + str(rank)
- elif args.run_intelligent:
- train_dir = os.path.join("cache", "output", "ckpt_{}".format(rank))
- data_dir = os.path.join("cache", "dataset")
- args.data_url = data_dir
- elif args.run_openi:
- train_dir = sync_data(args, environment="train")
- train_dir = os.path.join(train_dir, "ckpt_{}".format(rank))
-
- data = get_dataset(args)
- batch_num = data.train_dataset.get_dataset_size()
- optimizer = get_optimizer(args, net, batch_num)
- # save a yaml file to read to record parameters
-
- net_with_loss = get_train_one_step(args, net_with_loss, optimizer)
-
- eval_network = nn.WithEvalCell(net, criterion, args.amp_level in ["O2", "O3", "auto"])
- eval_indexes = [0, 1, 2]
- model = Model(net_with_loss, metrics={"acc", "loss"},
- eval_network=eval_network,
- eval_indexes=eval_indexes)
-
- config_ck = CheckpointConfig(save_checkpoint_steps=data.train_dataset.get_dataset_size()*10,
- keep_checkpoint_max=args.save_every)
- time_cb = TimeMonitor(data_size=data.train_dataset.get_dataset_size())
-
- ckpoint_cb = ModelCheckpoint(prefix=args.arch + str(rank), directory=train_dir,
- config=config_ck)
- loss_cb = LossMonitor(per_print_times=data.train_dataset.get_dataset_size())
- eval_cb = EvaluateCallBack(model, eval_dataset=data.val_dataset, src_url=train_dir,
- train_url=os.path.join(args.train_url, "ckpt_" + str(rank)),
- save_freq=args.save_every)
-
- print("begin train")
- model.train(int(args.epochs - args.start_epoch), data.train_dataset,
- callbacks=[time_cb, ckpoint_cb, loss_cb, eval_cb],
- dataset_sink_mode=args.dataset_sink_mode)
- print("train success")
-
- if not args.run_intelligent:
- if args.run_openi:
- sync_model(args)
- elif args.run_modelarts:
- import moxing as mox
- mox.file.copy_parallel(src_url=train_dir, dst_url=os.path.join(args.train_url, "ckpt_" + str(rank)))
-
-
- if __name__ == '__main__':
- main()
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